A research article entitled “Beyond Fine-Tuning: Leveraging Domain-Aware In-Context Learning with Large Language Models for Clinical Named Entity Recognition” has been published in the Journal of Biomedical Informatics (Volume 174, February 2026). The Journal of Biomedical Informatics is a leading international journal in the field of biomedical and clinical informatics that publish high-impact research at the intersection of medicine, data science, and artificial intelligence. Our study presents a comprehensive evaluation of large language model (LLM) based in-context learning (ICL) for clinical named entity recognition (NER) under realistic clinical constraints.

The study compared ICL with conventional encoder-based fine-tuning approaches using manually annotated real-world clinical notes. ICL configurations were optimized across task instructions, output formats, demonstration selection methods, sorting strategies, and demonstration pool sizes using the open-source LLaMA-3.3-70B model, while encoder-based fine-tuning was performed with RoBERTa-large as the strongest baseline.

The results showed that optimized demonstration selection played a major role in determining ICL performance, and improved macro F1 scores compared to random selection. In moderate-resource settings, ICL outperformed encoder-based fine-tuning and remained competitive across a wide range of labeled data sizes. Moreover, ICL demonstrated superior data efficiency and achieved substantial gains in cross-institutional transfer without requiring any parameter updates.

Overall, the study highlights the potential of domain-aware ICL as a flexible and data-efficient alternative to encoder-based fine-tuning for clinical NER. By enabling rapid adaptation through demonstration pools rather than retraining, the proposed approach offers a practical solution for real-world clinical natural language processing in dynamic and resource-limited healthcare environments.

This research was supported by grants from the MSIT (IITP), the Ministry of Health and Welfare (KHIDI), and the Ministry of Education (NRF) of the Republic of Korea.